PagerDuty
About PagerDuty
PagerDuty provides end-to-end incident management software. The product includes AI agents that automate incident response tasks. The platform is called the PagerDuty Operations Cloud. It connects to external tools and surfaces data through integrations. The company also offers the PD Reliability Platform as a product.
Products
- SRE Agent: Detects, triages, diagnoses, and fixes incidents on its own.
- Scribe Agent: Automatically documents incidents and generates postmortems.
- Shift Agent: Manages on-call handoffs and shift summaries.
- Insights Agent: Surfaces trends across incidents and identifies systemic issues.
- PD Reliability Platform: Provides automated incident detection, investigation and remediation by blended teams of agents and humans.
Used for
- Managing incidents directly from Slack or Microsoft Teams.
- Routing alerts to the right responder based on severity, history, and availability.
- Reducing alert noise using machine learning.
- Writing post-incident summaries and tracking action items.
Who it is for
- Used by practitioners and developers.
- Used by technical leaders.
- Used by developers, reliability engineers, customer success, security and the C-suite.
From PagerDuty's own website, read 2026-10-24. How we build listings.
How to evaluate AIOps & Incident Management
This is how the CIOPages Research Team evaluates this category. It is not an assessment of PagerDuty. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.
Incident Management & On-Call
25%
Alerting & On-Call Reliability
Does the page actually arrive? Multi-channel delivery (push, SMS, phone, email) with carrier redundancy, escalation if unacknowledged, on-call schedule and rotation math, override and coverage-gap handling, and the resilience of the notification pipeline itself — this is the one job that cannot fail
20%
Alert Noise Reduction & Correlation
Event grouping and de-duplication, intelligent correlation across signals, suppression and maintenance windows, dynamic routing by service ownership, and whether the platform measurably lowers pages-per-incident rather than just forwarding every alert your monitoring emits
20%
Incident Response & Comms Orchestration
Chat-native (Slack/Teams) war-room automation, roles and severity workflows, automatic timeline capture, stakeholder and status-page updates, runbook/automation actions, and how little manual coordination an incident commander has to do under pressure
15%
Post-Incident Learning & Analytics
Blameless retro/postmortem templates, auto-drafted timelines and summaries, action-item tracking to closure, MTTA/MTTR and on-call-load reporting, recurring-incident and follow-up trend analysis — the loop that actually reduces recurrence
10%
Integration & Ecosystem Fit
Depth of connectors to your monitoring/observability, ITSM, CI/CD, and chat; bidirectional ticket sync; webhook and API coverage for automation; and how naturally it slots into the tools your responders already live in rather than demanding a new home base
10%
AI / AI-SRE Capability
Autonomous triage and investigation agents, AI root-cause hypotheses and likely-culprit-commit detection, auto-generated summaries and retros, and scheduling/override agents — weighing genuine, evaluable assistance against demo-ware, and checking what data the AI is trained on and where it runs
IT Operations Management (ITOM)
25%
Event Correlation & Noise Reduction
Deduplication and flap suppression, cross-source correlation (topology-based, time-based, text/ML-based), how an alert storm collapses into a single situation, transparency of the correlation logic (open-box vs. black-box), and the realistic false-positive and missed-incident rate on YOUR event volume
20%
Discovery, Topology & CMDB
Agent and agentless discovery breadth (cloud, on-prem, network, containers, mainframe), automated service mapping and dependency graphs, how the topology is kept current as infrastructure churns, and whether correlation depends on a CMDB you must maintain or a graph the platform builds itself
20%
AIOps Depth: Root Cause & Remediation
Probable-root-cause identification and how it is explained, causal vs. purely statistical inference, anomaly and early-warning detection, closed-loop and agentic remediation, and how models behave on noisy, incomplete production data rather than curated demo streams
15%
Integration & Tool Coverage
Breadth of inbound monitoring/observability connectors, bidirectional ITSM and incident-tool integration (ServiceNow, Jira, PagerDuty), ChatOps/collaboration hooks, open APIs and webhooks, and whether the platform plays as an overlay or expects to own the stack
10%
Scale & Performance on Real Telemetry
Sustained events-per-second ingest, query and correlation latency at peak, multi-tenant and multi-region operation, data retention and cost at your volume, and graceful behavior during the event surge of a major incident — exactly when the platform matters most
10%
Operations, Workflow & Automation
Operator and NOC ergonomics, runbook and workflow automation, on-call and escalation routing, service-health and SLA reporting for business owners, role-based access, and audit logging of automated actions
Other AIOps & Incident Management Vendors
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Quick Facts
www.pagerduty.com Pricing pageCategoryObservability & Monitoring
SubcategoryAIOps & Incident Management
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